An integrated fuzzy-stochastic model for revenue management: The hospitality industry case

Revenue management aims at improving the performance of an organization by selling the right product/service to the right customer at the right time. This task is very dependent on uncontrollable external factors. In the hospitality industry, rooms of the hotel represent perishable assets and fixed capacities at the same time. Therefore, in the case of a stochastic process for customers calling in reservations prior to a particular booking date, a common problem for hotels is to devise a policy for maximizing the total expected profit conditional on the set of bookings. We propose a fuzzy model for the hotel revenue management under an uncertain and vague environment. Fuzziness of objective and constraint functions have been incorporated into a stochastic booking model considering multiple-day stays to show the effect of uncertainty on the optimal demand. By changing the relaxation parameters of the objective function, we have found a set of optimal solutions with, in most of the cases, a value of the objective function equal to the optimal solution of the stochastic model, providing several alternative optimal room allocations

A estratégia de overbooking e sua aplicação no mercado de transporte aéreo brasileiro

A key issue regarding airline management is related to the balance between demand and supply. It is a common situation to observe flights departing with a significant amount of empty seats, mainly due to no-show passengers and the very late canceling of reservations. In Brazil, airline passengers do not usually cancel reservations; on the contrary, they usually wait for the deadline to buy it or, if the ticket is already bought, they simply do not show-up. These practices, "allowed" by airlines due the flexibility offer in some fare-classes, impede the airline to sell those empty seats on time for flight departure. Therefore it becomes economically rational for the firms to protect from the losses by recurring to mechanisms such as the virtual increase in flight capacity, that is, overbooking. The present paper aims at developing an analysis of the efficiency of overbooking, discussing its advantages and disadvantages, risks, economic viability, and issues regarding responsibilities and regulation.Uma questão fundamental relativa à gestão de empresas aéreas diz respeito ao adequado balanceamento entre oferta e demanda. Grande parte dos vôos das companhias aéreas decola com um número significativo de assentos vazios, sendo uma parcela significativa dessa capacidade ociosa gerada pelos chamados passageiros no-show e pelos cancelamentos de reserva feitos com pouca antecedência em relação ao horário do vôo. No Brasil, o hábito de cancelar a reserva não é comum entre os usuários do transporte aéreo; na maioria das vezes, os passageiros esperam que o prazo da reserva expire ou, quando o bilhete já foi adquirido, simplesmente não comparecem ao embarque. Estas práticas, consentidas pelas empresas aéreas devido à flexibilidade oferecida em determinados bilhetes de passagem (classes tarifárias), inviabilizam a reutilização dos assentos que foram "desocupados" em tempo hábil para o vôo. Desta forma, torna-se economicamente racional que as empresas procurem se proteger das perdas resultantes adotando mecanismos como o aumento virtual da capacidade de uma aeronave, ou overbooking. O presente trabalho tem como objetivo realizar uma análise de eficiência e de riscos com relação à prática de overbooking, ou seja, verificar a viabilidade econômica e os riscos associados de se praticar tal estratégia, apontando suas vantagens e desvantagens, riscos associados e questões referentes às responsabilidades e à legislação

Overbooking in airline revenue management

Master'sMASTER OF SCIENC

Overbooking models for air cargo yield management

Master'sMASTER OF ENGINEERIN

APPLICATIONS OF REVENUE MANAGEMENT IN HEALTHCARE

Most profit oriented organizations are constantly striving to improve their revenues while keeping costs under control, in a continuous effort to meet customers‟ demand. After its proven success in the airline industry, the revenue management approach is implemented today in many industries and organizations that face the challenge of satisfying customers‟ uncertain demand with a relatively fixed amount of resources (Talluri and Van Ryzin 2004). Revenue management has the potential to complement existing scheduling and pricing policies, and help organizations reach important improvements in profitability through a better management of capacity and demand. The work presented in this thesis investigates the use of revenue management techniques in the service sector, when demand for service arrives from several competing customer classes and the amount of resource required to provide service for each customer is stochastic. We look into efficiently allocating a limited resource (i.e., time) among requests for service when facing variable resource usage per request, by deciding on the amount of resource to be protected for each customer and surgery class. The capacity allocation policies we develop lead to maximizing the organization‟s expected revenue over the planning horizon, while making no assumption about the order of customers‟ arrival. After the development of the theory in Chapter 3, we show how the mathematical model works by implementing it in the healthcare industry, more specifically in the operating room area, towards protecting time for elective procedures and patient classes. By doing this, we develop advance patient scheduling and capacity allocation policies and apply them to scheduling situations faced by operating rooms to determine optimal time allocations for various types of surgical procedures. The main contribution is the development of the methodology to handle random resource utilization in the context of revenue management, with focus in healthcare. We also develop a heuristics which could be used for larger size problems. We show how the optimal and heuristic-based solutions apply to real-life situations. Both the model and the heuristic find applications in healthcare where demand for service arrives randomly over time from various customer segments, and requires uncertain resource usage per request

Revenue Management for Strategic Alliances with Applications to the Airline Industry

Die Dissertation erscheint parallel im<a href="http://www.dr.hut-verlag.de/9783843902595.html"> Verlag Dr. Hut, München An airline has to decide whether to accept an incoming customer request for a seat in an airplane or to reject it in hope that another customer will request the seat later at a higher price. Within strategic alliances of airline partners this decision problem grows more complex. The presented work develops an option-based capacity control method, which dynamically decides on the acceptance or the rejection of customer requests to maximize the combined revenue of the alliance partners

Performance of multiple cabin optimization methods in airline revenue management

Thesis (S.M. in Operations Research)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 85-86).Although many airlines offer seats in multiple cabins (economy vs. premium classes) with different service quality, previous work on airline revenue management has focused on treating the cabins separately. In this thesis, we develop several single-leg multiple cabin revenue management optimization algorithms. We extend two different single-leg separate cabin dynamic programming algorithms to the multiple cabin case, and also present three Expected Marginal Seat Revenue (EMSR) based heuristics and a dynamic programming decomposition heuristic. We then evaluate the revenue and passenger mix performance of the different algorithms using the Passenger Origin-Destination Simulator (PODS) which simulates competitive markets with passenger choice of fare options and cabin. We first test the methods in a simple single market network and then in a more realistic complex network. We find that multiple cabin methods do not lead to a systematic revenue increase. Indeed, simulation results show that the performance of the different methods ranges from a decrease of 9.6% to an increase of 2.4% in revenues. The discrepancies in performance between the different methods are explained by the trade-off between revenue gains from additional economy bookings and the losses from displaced premium passengers. Further, we observe that successful methods lead to a revenue increase by accepting additional bookings in top economy classes rather than in low economy classes. Finally, the poor performance of the dynamic programming methods tested is due to a misalignment between the underlying assumptions of the algorithms and the reality of the booking and passenger choice process.by Pierre-Olivier Lepage.S.M.in Operations Researc

Optimization models for joint airline pricing and seat inventory control : multiple products, multiple periods

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 153-157).Pricing and revenue management are two essential levers to optimize the sales of an airline's seat inventory and maximize revenues. Over the past few decades, they have generated a great deal of research but have typically been studied and optimized separately. On the one hand, the pricing process focused on demand segmentation and optimal fares, regardless of any capacity constraints. On the other hand, researchers in revenue management developed algorithms to set booking limits by fare product, given a set of fares and capacity constraints. This thesis develops several approaches to solve for the optimal fares and booking limits jointly and simultaneously. The underlying demand volume in an airline market is modeled as a function of the fares. We propose an initial approach to the two-product, two-period revenue optimization problem by first assuming that the demand is deterministic. We show that the booking limit on sales of the lower-priced product is unnecessary in this case, allowing us to simplify the optimization problem. We then develop a stochastic optimization model and analyze the combined impacts of fares and booking limits on the total number of accepted bookings when the underlying demand is uncertain. We demonstrate that this joint optimization approach can provide a 3-4% increase in revenues from a traditional pricing and revenue management practices. The stochastic model is then extended to the joint pricing and seat inventory control optimization problem for booking horizons involving more than two booking periods, as is the case in reality. A generalized methodology for optimization is presented, and we show that the complexity of the joint optimization problem increases substantially with the number of booking periods. We thus develop three heuristics. Simulations for a three-period problem show that all heuristics outperform the deterministic optimization model. In addition, two of the heuristics can provide revenues close to those obtained with the stochastic model. This thesis provides a basis for the integration of pricing and revenue management. The combined effects of fares and booking limits on the number of accepted bookings, and thus on the revenues, are explicitly taken into account in our joint optimization models. We showed that the proposed approaches can further enhance revenues.by Claire Cizaire.Ph.D